High-content Profiling in Oesophageal Adenocarcinoma Identified Selectively Active Compounds for Repurposing and Novel Drug Discovery
Session type: Poster / e-Poster / Silent Theatre session
Oesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, driven by copy number alterations and large-scale genomic rearrangements. Such characteristics have hampered both therapeutic target discovery and success of targeted therapies in the clinic. We describe a phenotypic-led drug discovery platform using a multiparametric high-content profiling assay to identify active compounds and classify drug mechanism-of-action (MoA) across a panel of OAC cell lines as a novel strategy for discovery of new therapeutic targets and repurposing existing drug classes for OAC.
We prioritized a panel of six OAC lines that represent the genetic diversity of OAC, a pre-neoplastic Barret’s oesophagus line, and a non-transformed squamous oesophageal line. We applied Cell Painting, a multiplexed fluorescent dye assay to profile the phenotypic response of the cell lines to 20,000 small molecules and approved drugs. We trained a machine-learning (ML) model to predict MoA using phenotypic fingerprints from a library of reference compounds covering distinct mechanistic classes.
From a subset of 3,000 approved drugs we have validated 46 compounds following dose-response confirmation. Clustering the phenotypic response to these molecules identified a number of phenotypic clusters enriched with similar pharmacological classes. We identified the drug Methotrexate and three other dihydrofolate reductase (DHFR) inhibitors as highly selective for the OAC lines compared to the Barrett’s and normal lines. In addition, the approved HDAC inhibitors, Belinostat, Panobinostat and Quisinostat demonstrate potent activity on OAC cell lines. Multiparametric phenotypic dose response profiles of the DHFR inhibitors cluster with DNA damaging agents and ML predicts them to be DNA damaging agents while the HDAC inhibitors cluster with several other HDAC inhibitors. We further identify a number of compound from our diverse chemical library which show potent and selective activity for OAC cells and which do not cluster with the reference library of known MoA, indicating they may be targeting novel oesopahageal cancer biology.
Integration of the phenotypic data with genetic data across our panel of diverse cell lines and proteomic and transcriptomic pathway analysis, pre- and post-treatment, are ongoing and may provide further insight into drug selectively and the basis for future biomarker-based clinical trials in OAC.